Triple
T19587228
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | New York State Rifle & Pistol Association, Inc. v. Bruen |
E307116
|
entity |
| Predicate | impactsAreaOfLaw |
P113669
|
FINISHED |
| Object | gun control regulation |
—
|
LITERAL FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: gun control regulation | Statement: [New York State Rifle & Pistol Association, Inc. v. Bruen, impactsAreaOfLaw, gun control regulation]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: impactsAreaOfLaw Context triple: [New York State Rifle & Pistol Association, Inc. v. Bruen, impactsAreaOfLaw, gun control regulation]
-
A.
impactOnLaw
Indicates the effect or influence that one entity, event, or action has on laws, legal rules, or the legal system.
-
B.
notableAreaOfLaw
Indicates that a person or entity is particularly recognized or distinguished in a specific field or area of law.
-
C.
legalArea
Indicates the specific field or branch of law that a legal matter, case, or document pertains to.
-
D.
appliesToFieldOfLaw
chosen
Indicates that something is relevant or applicable to a particular field or branch of law.
-
E.
branchOfLaw
Indicates a relationship where one legal field or discipline is a subdivision or specialized area within a broader body of law.
- F. None of above.
Provenance (3 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69d8e510024481908415c0d616fa6186 |
completed | April 10, 2026, 11:54 a.m. |
| NER | Named-entity recognition | batch_69e640523d10819091320a61456f6437 |
completed | April 20, 2026, 3:03 p.m. |
| PD | Predicate disambiguation | batch_69e514dbdb988190b55931a8138c73e7 |
completed | April 19, 2026, 5:46 p.m. |
Created at: April 10, 2026, 1:43 p.m.